Completed Courses & Training
A curated collection of completed online courses and training programs in deep learning, machine learning operations, and Generative AI.
Neural Networks and Deep Learning
Deep Learning & Neural Networks
Foundational course covering neural networks, forward and backpropagation, and optimization. Introduces the practical aspects of implementing deep neural networks from scratch.
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Deep Learning & Neural Networks
Advanced techniques for improving neural network performance, including hyperparameter optimization, regularization methods, and various optimization algorithms for faster convergence.
Structuring Machine Learning Projects
Deep Learning & Neural Networks
Best practices for organizing and managing ML projects, including diagnostic strategies, bias-variance tradeoffs, and how to make decisions in an ML project pipeline.
Sequence Models
Deep Learning & Neural Networks
Covers RNNs, LSTMs, and attention mechanisms for processing sequential data. Includes applications in time series, machine translation, and speech recognition.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
TensorFlow in Practice
Practical introduction to TensorFlow framework with hands-on experience building and training neural networks for computer vision and other applications.
Convolutional Neural Networks in TensorFlow
TensorFlow in Practice
Deep dive into convolutional neural networks for image processing, feature extraction, and computer vision tasks using TensorFlow and Keras.
Natural Language Processing in TensorFlow
TensorFlow in Practice
NLP fundamentals using TensorFlow, including text classification, tokenization, embeddings, and building language models with RNNs and LSTMs.
Sequences, Time Series and Prediction
TensorFlow in Practice
Time series analysis and forecasting with neural networks, including techniques for handling temporal data and making predictions on sequential patterns.
TensorFlow in Practice Specialization
TensorFlow in Practice
Comprehensive specialization covering practical TensorFlow skills including CNNs, NLP, time series prediction, and real-world application development.
Introduction to Machine Learning in Production
Machine Learning Operations
Overview of ML production systems, data engineering for ML, and the ML lifecycle from problem definition through deployment and monitoring.
Machine Learning Data Lifecycle in Production
Machine Learning Operations
Deep dive into data engineering for ML production, including data collection, labeling, validation, and management in real-world ML pipelines.
Machine Learning
Generative AI & Foundations
Comprehensive introduction to machine learning fundamentals, supervised and unsupervised learning, and practical algorithms for real-world problems.
Generative AI with Large Language Models
Generative AI & Foundations
Introduction to generative AI and LLMs, covering transformer architectures, prompt engineering, fine-tuning, and practical applications of large language models.
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